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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth: A Case Study Appendix

Window Frame

As mentioned in the earlier sections discussing the basics of window functions, by default, a window is set for each value group to encompass all the rows from the first to the current row in the partition, as shown in Figure 5.6. However, this is the default and can be adjusted using the window frame clause. A window function query using the window frame clause would look as follows:

SELECT 
  {columns},
  {window_func} OVER (
    PARTITION BY {partition_key} 
    ORDER BY {order_key} 
    {rangeorrows} BETWEEN {frame_start} AND {frame_end}
  )
FROM 
  {table1};

Here, {columns} are the columns to retrieve from tables for the query, {window_func} is the window function you want to use, {partition_key} is the column or columns you want to partition on, {order_key} is the column or columns you want to order by, {rangeorrows} is either the RANGE keyword or the ROWS keyword...

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